Parklet: A New Urban Platform for Emergent Forms of Communication and Social Interaction in the Cities
Bibliographic record
Abstract
Social interaction is critical to the physical and intellectual well-being of a functioning democracy. The excessive influence of technology and lack of urban design and planning’s attention to pedestrian experience has caused our interactions to become more private, isolated, and mostly virtual. The following project presents the product of a design-build studio which uses adaptive/Kinetic systems to generate a creative solution to this social problem. Specifically, it will showcase the efforts of students working on a Parklet project, repurposing urban space to advance local business ethics and social justice issues. The Parklet replaces a parking space, fostering increases in social connections, public vibrancy, and support for local businesses. To move beyond schematic design, and offer students an experiment in real-world design issues, this studio provided a hands-on atmosphere for collaborative and consensus design experience, learning-by-doing, detailing challenges, and offsite construction strategies. It was structured to promote lessons in collaboration, construction detailing and process, and adaptive design, including working with city officials to meet code and zoning regulations. Since the project’s site is located in a neighboring city, students prefabricated the pieces in school and shipped and assembled them on site in one day. The studio sought to promote CAAD education, teaching design, and construction, but also innovation and entrepreneurship, through computational technology. The pedagogical framework was defined around various considerations such as structural, functional, financial, aesthetical, technological, and collaboration with other disciplines such as structural engineering and construction management
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".